{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import pickle as pkl\n",
    "import time\n",
    "from random import shuffle\n",
    "import pandas as pd \n",
    "import spectral\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab as pl\n",
    "import scipy\n",
    "import IndianPinesCNN\n",
    "#import seaborn as sns\n",
    "from collections import Counter\n",
    "import Spatial_dataset as input_data\n",
    "import patch_size\n",
    "import os\n",
    "#print IndianPinesCNN.IMAGE_PIXELS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import scipy.io as io\n",
    "DATA_PATH = os.path.join(os.getcwd(),\"Data\")\n",
    "input_image = scipy.io.loadmat(os.path.join(DATA_PATH, 'Indian_pines.mat'))['indian_pines']\n",
    "output_image = scipy.io.loadmat(os.path.join(DATA_PATH, 'Indian_pines_gt.mat'))['indian_pines_gt']\n",
    "\n",
    "model_name = 'model-spatial-11X11.ckpt-3999'\n",
    "# input_image = np.rot90(input_image)\n",
    "# output_image = np.rot90(output_image)\n",
    "height = output_image.shape[0]\n",
    "width = output_image.shape[1]\n",
    "PATCH_SIZE = patch_size.patch_size\n",
    "batch_size = 100\n",
    "num_examples = 800\n",
    "\n",
    "conv1 = 500\n",
    "conv2 = 100\n",
    "fc1 = 200\n",
    "fc2 = 84"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## Scaling Down the image to 0 - 1\n",
    "\n",
    "input_image = input_image.astype(float)\n",
    "input_image -= np.min(input_image)\n",
    "input_image /= np.max(input_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def mean_array(data):\n",
    "    mean_arr = []\n",
    "    for i in range(data.shape[0]):\n",
    "        mean_arr.append(np.mean(data[i,:,:]))\n",
    "    return np.array(mean_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def Patch(data,height_index,width_index):\n",
    "    transpose_array = data.transpose((2,0,1))\n",
    "    #print transpose_array.shape\n",
    "    height_slice = slice(height_index, height_index+PATCH_SIZE)\n",
    "    width_slice = slice(width_index, width_index+PATCH_SIZE)\n",
    "    patch = transpose_array[:, height_slice, width_slice]\n",
    "    #print patch.shape\n",
    "    mean = mean_array(transpose_array)\n",
    "    mean_patch = []\n",
    "    for i in range(patch.shape[0]):\n",
    "        mean_patch.append(patch[i] - mean[i])\n",
    "    mean_patch = np.asarray(mean_patch)\n",
    "    patch = mean_patch.transpose((1,2,0))\n",
    "    patch = patch.reshape(-1,patch.shape[0]*patch.shape[1]*patch.shape[2])\n",
    "    #print patch.shape\n",
    "    return patch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def placeholder_inputs(batch_size):\n",
    "    \"\"\"Generate placeholder variables to represent the input tensors.\n",
    "    These placeholders are used as inputs by the rest of the model building\n",
    "    code and will be fed from the downloaded data in the .run() loop, below.\n",
    "    Args:\n",
    "    batch_size: The batch size will be baked into both placeholders.\n",
    "    Returns:\n",
    "    images_placeholder: Images placeholder.\n",
    "    labels_placeholder: Labels placeholder.\n",
    "    \"\"\"\n",
    "    # Note that the shapes of the placeholders match the shapes of the full\n",
    "    # image and label tensors, except the first dimension is now batch_size\n",
    "    # rather than the full size of the train or test data sets.\n",
    "    images_placeholder = tf.placeholder(tf.float32, shape=(batch_size, IndianPinesCNN\n",
    "                                                           .IMAGE_PIXELS))\n",
    "    labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size))\n",
    "    return images_placeholder, labels_placeholder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fill_feed_dict(data_set, images_pl, labels_pl):\n",
    "    \"\"\"Fills the feed_dict for training the given step.\n",
    "    A feed_dict takes the form of:\n",
    "    feed_dict = {\n",
    "      <placeholder>: <tensor of values to be passed for placeholder>,\n",
    "      ....\n",
    "    }\n",
    "    Args:\n",
    "    data_set: The set of images and labels, from input_data.read_data_sets()\n",
    "    images_pl: The images placeholder, from placeholder_inputs().\n",
    "    labels_pl: The labels placeholder, from placeholder_inputs().\n",
    "    Returns:\n",
    "    feed_dict: The feed dictionary mapping from placeholders to values.\n",
    "    \"\"\"\n",
    "    # Create the feed_dict for the placeholders filled with the next\n",
    "    # `batch size ` examples.\n",
    "    images_feed, labels_feed = data_set.next_batch(batch_size)\n",
    "    feed_dict = {\n",
    "      images_pl: images_feed,\n",
    "      labels_pl: labels_feed,\n",
    "    }\n",
    "    return feed_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def do_eval(sess,\n",
    "            eval_correct,\n",
    "            images_placeholder,\n",
    "            labels_placeholder,\n",
    "            data_set):\n",
    "    \"\"\"Runs one evaluation against the full epoch of data.\n",
    "    Args:\n",
    "    sess: The session in which the model has been trained.\n",
    "    eval_correct: The Tensor that returns the number of correct predictions.\n",
    "    images_placeholder: The images placeholder.\n",
    "    labels_placeholder: The labels placeholder.\n",
    "    data_set: The set of images and labels to evaluate, from\n",
    "      input_data.read_data_sets().\n",
    "    \"\"\"\n",
    "    # And run one epoch of eval.\n",
    "    true_count = 0  # Counts the number of correct predictions.\n",
    "    steps_per_epoch = data_set.num_examples // batch_size\n",
    "    num_examples = steps_per_epoch * batch_size\n",
    "    for step in xrange(steps_per_epoch):\n",
    "        feed_dict = fill_feed_dict(data_set,\n",
    "                                   images_placeholder,\n",
    "                                   labels_placeholder)\n",
    "        true_count += sess.run(eval_correct, feed_dict=feed_dict)\n",
    "    precision = float(true_count) / num_examples\n",
    "    print('  Num examples: %d  Num correct: %d  Precision @ 1: %0.04f' %\n",
    "        (num_examples, true_count, precision))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def decoder():\n",
    "    \n",
    "    #data_sets = input_data.read_data_sets('Test1.mat','test')\n",
    "    \n",
    "    with tf.Graph().as_default():\n",
    "        \n",
    "        images_placeholder, labels_placeholder = placeholder_inputs(1)\n",
    "\n",
    "        logits = IndianPinesCNN.inference(images_placeholder,\n",
    "                                 conv1,\n",
    "                                 conv2,        \n",
    "                                 fc1,\n",
    "                                 fc2)\n",
    "        \n",
    "        eval_correct = IndianPinesCNN.evaluation(logits, labels_placeholder)\n",
    "        \n",
    "        sm = tf.nn.softmax(logits)\n",
    "        \n",
    "        saver = tf.train.Saver()\n",
    "\n",
    "        sess = tf.Session()\n",
    "        \n",
    "        saver.restore(sess,model_name)\n",
    "        \n",
    "        temp = []\n",
    "\n",
    "        outputs = np.zeros((height,width))\n",
    "        predicted_results = [[0 for i in range(width)]for x in range(height)]\n",
    "        for i in range(height-PATCH_SIZE+1):\n",
    "            for j in range(width-PATCH_SIZE+1):\n",
    "                target = int(output_image[i+PATCH_SIZE/2, j+PATCH_SIZE/2])\n",
    "                if target == 0 :\n",
    "                    continue\n",
    "                else :\n",
    "                    image_patch=Patch(input_image,i,j)\n",
    "                    #print image_patch\n",
    "                    prediction = sess.run(sm, feed_dict = {images_placeholder:image_patch})\n",
    "                    #print prediction\n",
    "                    temp1 = np.argmax(prediction)+1\n",
    "                    #print temp1\n",
    "                    outputs[i+PATCH_SIZE/2][j+PATCH_SIZE/2] = temp1\n",
    "                    predicted_results[i+PATCH_SIZE/2][j+PATCH_SIZE/2] = prediction\n",
    "                    \n",
    "    return outputs,predicted_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"conv_1/Conv2D:0\", shape=(1, 9, 9, 500), dtype=float32)\n",
      "Tensor(\"h_conv2/h_conv2:0\", shape=(1, 3, 3, 100), dtype=float32)\n",
      "Tensor(\"h_pool2:0\", shape=(1, 2, 2, 100), dtype=float32)\n",
      "Tensor(\"Reshape:0\", shape=(1, 400), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "predicted_image,predicted_results = decoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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l6QmbOSvplayEmqsguzDsyxqbm0wCY2YxgY6js5+bIEoBajK/FFTDqrugeqvZ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\nQoiM8v8D8d8SwxjpbL0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3c8118690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pXeSPYcxkaYgkqCOBnjUYmcbHkXxerA8dw36skbqJS1S7VxY5VAls1jx/udjC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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3b835cdd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ground_truth = spectral.imshow(classes = output_image,figsize =(5,5))\n",
    "predict_image = spectral.imshow(classes = predicted_image.astype(int),figsize =(5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# f_out = open('Predictions.pkl','ab')\n",
    "# pkl.dump({'11x11_aug':predicted_results}, f_out)\n",
    "# f_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
